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Journal Article•DOI•

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

Samarjit Kar1, Sujit Das2, Pijush Kanti Ghosh2•
National Institute of Technology, Durgapur1, Dr. B.C. Roy Engineering College, Durgapur2
01 Feb 2014-Vol. 15, pp 243-259
TL;DR: The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
Citations
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Journal Article•DOI•
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

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Dervis Karaboga1, Ebubekir Kaya2•
Erciyes University1, Nevşehir University2
01 Dec 2019-Artificial Intelligence Review
TL;DR: The heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study and it has been observed that there is a trend toward heuristic based ANfIS training algorithms for better performance recently.
Abstract: In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

454 citations

Cites background from "Review Article: Applications of neu..."

  • ...Because of this structure, it is used in solving many real world problems (Kar et al. 2014)....

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Journal Article•DOI•
Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

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Xianbo Xiang1, Caoyang Yu1, Qin Zhang1•
Huazhong University of Science and Technology1
01 Aug 2017-Computers & Operations Research
TL;DR: Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances.

234 citations

Journal Article•DOI•
The application of ANFIS prediction models for thermal error compensation on CNC machine tools

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Ali M. Abdulshahed1, Andrew P. Longstaff1, Simon Fletcher1•
University of Huddersfield1
01 Feb 2015
TL;DR: This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models that show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules.
Abstract: This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

219 citations

Cites background or methods from "Review Article: Applications of neu..."

  • ...Different learning techniques, such as a hybrid-learning algorithm [14] or genetic algorithm (GA) [24], can be adopted to solve this training problem....

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  • ...ANFIS techniques have already been applied to different engineering areas such as support to decision-making [13,14], modelling tool wear in turning process [15], and modelling thermal errors in machine tools [8,16]....

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Journal Article•DOI•
Recent advances in neuro-fuzzy system: A survey

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K V Shihabudheen1, G. N. Pillai1•
Indian Institute of Technology Roorkee1
15 Jul 2018-Knowledge Based Systems
TL;DR: A review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017 is proposed to help readers have a general overview of the state-of-the-arts of neuro- fizzy systems and easily refer suitable methods according to their research interests.
Abstract: Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific and engineering areas due to its effective learning and reasoning capabilities. The neuro-fuzzy systems combine the learning power of artificial neural networks and explicit knowledge representation of fuzzy inference systems. This paper proposes a review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017. The main purpose of this survey is to help readers have a general overview of the state-of-the-arts of neuro-fuzzy systems and easily refer suitable methods according to their research interests. Different neuro-fuzzy models are compared and a table is presented summarizing the different learning structures and learning criteria with their applications.

168 citations

Journal Article•DOI•
Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

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Paulo Vitor de Campos Souza1•
Johannes Kepler University of Linz1
01 Jul 2020-Applied Soft Computing
TL;DR: It is concluded that the fuzzy neural network models and their derivations are efficient in constructing a system with a high degree of accuracy and an appropriate level of interpretability working in a wide range of areas of economics and science.

133 citations

References
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Journal Article•DOI•
ANFIS: adaptive-network-based fuzzy inference system

[...]

Jyh-Shing Roger Jang1•
University of California, Berkeley1
01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

15,085 citations

Journal Article•DOI•
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

[...]

İnan Güler1, Elif Derya Übeyli2•
Gazi University1, TOBB University of Economics and Technology2
30 Oct 2005-Journal of Neuroscience Methods
TL;DR: The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANfIS model has potential in classifying the EEG signals.

524 citations

"Review Article: Applications of neu..." refers methods in this paper

  • ...[15] proposed adaptive neuro fuzzy inference system (ANFIS) model for classification of electro encephalogram (EEG) signals, which was clinically used to investigate brain disorder....

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  • ...[15] (2005) Wavelet transform, ANFIS Brain disorder Classification...

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Journal Article•DOI•
An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

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Kemal Polat1, Salih Güneş1•
Selçuk University1
01 Jul 2007-Digital Signal Processing
TL;DR: The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS using adaptive neuro-fuzzy inference system and it was very promising with regard to the other classification applications in literature for this problem.

369 citations

"Review Article: Applications of neu..." refers background or methods in this paper

  • ...[18] K. Polat, K. Gunes, An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease, Digital Signal Processing 17 (4) (2007) 702–710....

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  • ...Authors/years Methodology Domain Article type Neagoe et al. [12] (2003) Fuzzy-Gaussion neural network, Ischemic heart disease Experimental Mastorocostas and Hilas [13] (2004) FNN, least square methods Lung sound analysis Analytical Mastorocostas & Theocharis [14] (2005) Recurrent filter, fuzzy neural network Lung sounds separation Experimental Guler et al. [15] (2005) Wavelet transform, ANFIS Brain disorder Classification Oweis et al. [16] (2005) NF approach Bio-medical Classification Subasi [30] (2006) Neuro-fuzzy logic technique, discrete Epileptic seizure Analytical Wavelet transform Stavrakoudis et al. [17] (2007) Fuzzy neural filter, TSK fuzzy network Lung soung separation Experimental Polat & Gunes [18] (2007) PCA, ANFIS, expert system Diabetes diagnosis Diagnosis Sengur [19] (2008) ANFIS, LDA Heart disease diagnosis Comparative Ubeyli [20] (2009) ANFIS, decision making, Lyapunov-exponent ECG signal classification Classification Ovreiu and Simon [21] (2010) EA, BPO, NF Rules Cardiac disease Simulation Alamelumangai and DeviShree [22] (2010) Neuro fuzzy model, PSO Breast cancer diagnosis Optimization Obi and Imainvan [23] (2011) NFI procedure Alzheimer Diagnosis Obi and Imianvan [24] (2011) NFI procedure Leukemia Diagnosis Kumar et al. [25] (2011) Fast ANFIS, LM Algorithm Cancer diagnosis Experimental Agboizebeta and Chukwuyeni [26] (2012) Neuro-fuzzy inference (NFI) system Thyroid detection Demonstrating d c r i A n s d o ( d p m s s d y n p t t a p 2 d o t c f d h o Agboizebeta and Chukwuyeni [27] (2012) NFI procedure Ephzibah and Sundarapandian [28] (2012) GA, ANN Khameneh et al. [29] (2012) ANFIS iagnostic accuracy of diabetes disease....

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  • ...Polat & Gunes [18] (2007) PCA, ANFIS, expert system Diabetes diagnosis Diagnosis...

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  • ...In the same year (2007), Polat and Gunes [18] paid their attention for diabetes patients....

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Journal Article•DOI•
Forecasting stock market short-term trends using a neuro-fuzzy based methodology

[...]

George S. Atsalakis1, Kimon P. Valavanis2•
Technical University of Crete1, University of Denver2
01 Sep 2009-Expert Systems With Applications
TL;DR: Real case studies using data from emerging and well developed stock markets to train and evaluate the proposed neuro-fuzzy system illustrate that compared to the ''buy and hold'' strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far superior.
Abstract: A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive neuro-fuzzy technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the Efficient Market Hypothesis (EMH) by demonstrating much improved and better predictions, compared to other approaches, of short-term stock market trends, and in particular the next day's trend of chosen stocks. The ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifteen different combinations of past stock prices performed to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Root Mean Square Error (RMSE). Gaussian-2 shaped membership functions are chosen over bell shaped Gaussian and triangular ones to fuzzify the system inputs due to the lowest RMSE. Real case studies using data from emerging and well developed stock markets - the Athens and the New York Stock Exchange (NYSE) - to train and evaluate the proposed system illustrate that compared to the ''buy and hold'' strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far superior.

282 citations

"Review Article: Applications of neu..." refers background in this paper

  • ...Authors/years Methodology Domain Article type Ang and Quek [31] (2005) RSPOP algorithm, rough set, NF Stock market Experimental Lin et al. [32] (2006) Hybrid NF method English auction Experimental Atsalakis and Valavanis [36] (2009) ANFIS Stock market Simulation Gumus et al. [35] (2009) MILP, ANN Supply chain network Comparative Kablan [33] (2009) (2011) ANFIS Financial trading market Simulation Nowroozi et al. [34] (2009) NFS Gas condensate Experimental Mordjaoui et al. [37] (2010) NF technique, modeling Electrical load forecasting Industrial context Kalbande et al. [43] (2011) NF algorithm Toll collection Case study Iranmanesh et al. [38] (2011) LLNF Energy consumption Case study Schott and Kalita [39] (2011) NF Logic Stock market Experimental Tsai and Chen [174] (2011) Fuzzy delphi, fuzzy AHP Tourism industry Development Kasa [40] (2012) FL, NN Economical system Simulation Giovanis [42] (2012) ANFIS, Gaussian Economic (USA) Case study s n b t m p s g d j m m l n A f t fi t m m q n c i n m o c e t u o h t i s 5 t a t a i i n Fang [41] (2012) ANFIS Liu et al. [157] (2012) Fuzzy logic, decision analysi Lin et al. [154] (2012) Kalman filter decision system euro fuzzy system can predict the final price accurately much etter than the others....

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  • ...Atsalakis and Valavanis [36] (2009) ANFIS Stock market Simulation...

    [...]

  • ...Atsalakis and Valavanis [36] also irected their research on stock market using ANFIS....

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  • ...[36] G.S. Atsalakis, K.P. Valavanis, Forecasting stock market short-term trends using a neuro-fuzzy based methodology, Expert Systems with Applications 36 (2009) 10696–10707....

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Book•
Flexible neuro-fuzzy systems

[...]

Leszek Rutkowski, Krzysztof Cpałka
01 Jan 2004
TL;DR: This approach introduces more flexibility to the structure and design of neuro-fuzzy systems, and shows that Mamdani- type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.
Abstract: In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.

268 citations

"Review Article: Applications of neu..." refers methods in this paper

  • ...Next year, Rutkowski and Cpalka [115] derived a neuro-fuzzy structures, which is called flexible neuro-fuzzy inference systems based on input-output data with the help of Mamdani and logical approach where along with the parameters, system type (Mamdani or logical) was also learned....

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  • ...[115] (2003) Mamdani and logical NF approach...

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  • ...[115] L. Rutkowski, K. Cpalka, Flexible Neuro-Fuzzy Systems, IEEE Transactions on Neural Networks 14 (3) (2003) 554–574....

    [...]

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Trending Questions (1)
Can you provide outline for review paper topic?

The paper provides a review and classification of neuro fuzzy systems (NFS) applications into ten different categories, with a brief future outline for each category.

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